Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits

Abstract

Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874-1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.2 of 13 oleaster fruits, as a traditional medicine, are used to treat many diseases in nations and countries from Central Asia to West Asia. As a medicine and food, the fruit of narrow-leaved oleaster fruits is not only a raw material for food industry processing but also a raw material for functional food and new drugs [1][2][3][4][5][6][7][8][9][10][11]. It has good prospects for development and utilization in arid and semi-arid regions of Northwest China. Its unique habitat environment and long history of planting have produced unique qualities of narrow-leaved oleaster fruits in different producing areas. The qualities of narrow-leaved oleaster fruits are different depending on their place of origin, so it is urgent to establish effective methods for identification of the place of origin of narrow-leaved oleaster fruits.At present, different scholars have isolated the bioactive components of narrow-leaved oleaster fruits [12], studied the physical and chemical properties and antioxidant properties of narrow-leaved oleaster fruits [13], used Gas Chromatography-Mass Spectrometer (GC-MS) to analyze the components of narrow-leaved oleaster fruit oil [14], and studied the diseases of narrow-leaved oleaster fruits [15]. However, there have been few studies on differentiation of the origins of narrow-leaved oleaster fruits. It is feasible to differentiate narrow-leaved oleaster fruits from different producing areas by synthesizing external morphological and microscopic characteristics and physicochemical identification of fruit powder...

Rapid Vitality Estimation and Prediction of Corn Seeds Based on Spectra and Images Using Deep Learning and Hyperspectral Imaging Techniques

“…In the hyperspectral field, CNN has been used to classify one-dimensional spectral data, twodimensional images, and three-dimensional hyperspectral remote sensing data. Using PLS-DA, SVM, and CNN to establish a discriminant model for the identification of narrow-leaved oleaster geographical origins, the classification accuracy exceeded 90% [25]. Nie et al [26] used the same three models to distinguish hybrid okra seeds and hybrid loofah seeds.…”

Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods

“…SVM can achieve approximate implementation of structural risk minimization to avoid overfitting [23]. Owing to its surprising classification ability, SVM has been widely used in classification issues [24]. In SVM, the original low dimensional data are first mapped into a higher dimensional space through a nonlinear mapping function.…”

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

“…Gene markers [192] Transcriptome assembly [193] Genome Resequencing [194] Elaeagnus angustifolia Russian olive or wild olive Geographic study using machine learning [195] Hi-C assembly [196] Transciptome profiling [197] Plant signalling regarding salt [198] Ensete ventricosum Ethiopian Banana…”